Dice Similarity Measure Research Articles

Some novel Dice similarity measures for picture fuzzy sets and their applications

The similarity measures, as an important tool for assessing the similarity between picture fuzzy sets, have attracted the attention of numerous scholars and achieved rich achievements. Although existing similarity measures have exhibited their unique research contributions, they still fail to distinguish between highly similar but different picture fuzzy sets. This phenomenon poses a major challenge in applications, limiting our ability to make accurate judgments in scenarios that necessitate meticulous differentiation of picture fuzzy sets. Furthermore, the current research focuses on proposing individual and specific calculation formulas, particularly in the realm of Dice similarity measures. However, the systematic, uniform construction of Dice similarity measures remains mostly untapped. The main objective of this article is to propose a method for constructing Dice similarity measures. Through this approach, we can not only integrate existing Dice similarity measures into a unified framework, but also generate novel similarity measures. Firstly, we introduce a function that satisfies specific conditions as the basis for constructing Dice similarity measures. Secondly, by selecting different functions, we obtain various similarity measures. Finally, we propose the pattern recognition algorithm, the face recognition algorithm and the cluster analysis algorithm, all based on the proposed similarity measures. These algorithms are then applied to various fields, including pattern recognition, face recognition, and clustering analysis. Experimental results demonstrate that the proposed similarity measures not only compensate for the shortcomings of existing similarity measures but also exhibit high reliability and practicality, offering a powerful new tool for research and applications in related fields.

Group decision making based on dice similarity measure and 2-dimension linguistic fuzzy weighted arithmetic aggregation operator for 2-dimension linguistic fuzzy variables

In the process of decision-making, it usually applies 2-dimension linguistic fuzzy variable (2DLFV) to describe the uncertainty caused by the decision objects and the decision makers. This proposal aims to design group decision making method based on dice similarity measure (DSM) and 2-dimension linguistic fuzzy weighted arithmetic aggregation (2DLFWAA) operator for 2-dimension linguistic fuzzy framework. Based on the psychological theory and prospect theory, 2-dimension linguistic scale function (2DLSF) about 2DLFV is firstly proposed, and new operational rules of 2DLFV are introduced. Two forms of DSM are proposed to measure the closeness between two 2-dimension linguistic fuzzy sets (2DLFSs). A definition is given for 2DLFWAA operator and some of its properties are studied. Based on the DSM and the 2DLFWAA operator for 2DLFSs, the technique for order of preference by similarity to ideal solution (TOPSIS) method is extended to handle the multiple attribute group decision making (MAGDM) problems under 2-dimension linguistic fuzzy environments. Finally, an illustrative example is provided to show the concrete process of the proposed method. Comparative analysis and sensitivity analysis are conducted to test the rationality and robust of the proposed method. The results suggest that the proposed method offers a flexible and reliable approach in 2-dimension linguistic fuzzy scenarios.

Modern Approach in Pattern Recognition Using Circular Fermatean Fuzzy Similarity Measure for Decision Making with Practical Applications

The circular Fermatean fuzzy (CFF) set is an advancement of the Fermatean fuzzy (FF) set and the interval-valued Fermatean fuzzy (IVFF) set which deals with uncertainty. The CFF set is represented as a circle of radius ranging from 0 to 2 with the center at the degree of association (DA) and degree of nonassociation (DNA). If multiple people are involved in making decisions, the CFF set, as an alternative to the FF and IVFF sets, can deal with ambiguity more effectively by encircling the decision values within a circle rather than taking an average. Using algorithms, a pattern can be observed computationally or visually. Machine learning algorithm utilizes pattern recognition as an instrument for identifying patterns and also similarity measure (SM) is a beneficial pattern recognition tool used to classify items, discover variations, and make future predictions for decision making. In this work, we introduce the CFF cosine and Dice similarity measures (CFFDMs and CFFSMs), and their properties are studied. Unlike traditional approaches of decision making, which emphasize a single number, the proposed CFFSMs observe the pattern over the circular region to help in dealing with uncertainty more effectively. We introduce an innovative decision-making method in the FF setting. Available bank loans and applicants’ eligibility levels are represented as CFF set using their FF criteria and are taken as loan patterns and customer eligibility patterns. The loan is allocated to the applicant by measuring the CFFCSM and CFFDSM between the two patterns. Also, laptops are suggested to the customers by measuring the similarity between specification pattern and requirement pattern. The correctness and consistency of the proposed models are ensured by comparison analysis and graphical simulations of the input and similarity CFFNs.

An Optimized Semantic Segmentation Framework for Human Skin Detection

The study incorporating optimization strategy in semantic segmentation is underexplored in dermatology. Existing approaches used complex and various heuristic designs of image processing algorithms and deep models customized for skin detection problems. This paper demonstrates Particle Swarm Optimization (PSO)-incorporated AlexNet framework for the skin segmentation task. The results from testing the trained model are promising. The model produced satisfactory performances even with a strict split of 50 %, confirming the high efficiency of the proposed framework. The mean Jaccard index and Dice similarity measures evaluated between the annotated and predicted mask ranged from 0.80 to 0.93 in the binary classification of pixels as “skin” versus “background”. This work identified that the location and color variability of skin pixels in the training data are crucial to obtaining a good skin segmentation performance. Further works that can be explored in this area include adopting a robust preprocessing strategy to increase data variability and improve model generalization or implementing an optimization-enhanced strategy on the existing segmentation models for comparison.

Cross-modal tumor segmentation using generative blending augmentation and self-training.

Data scarcity and domain shifts lead to biased training sets that do not accurately represent deployment conditions. A related practical problem is cross-modal image segmentation, where the objective is to segment unlabelled images using previously labelled datasets from other imaging modalities. We propose a cross-modal segmentation method based on conventional image synthesis boosted by a new data augmentation technique called Generative Blending Augmentation (GBA). GBA leverages a SinGAN model to learn representative generative features from a single training image to diversify realistically tumor appearances. This way, we compensate for image synthesis errors, subsequently improving the generalization power of a downstream segmentation model. The proposed augmentation is further combined to an iterative self-training procedure leveraging pseudo labels at each pass. The proposed solution ranked first for vestibular schwannoma (VS) segmentation during the validation and test phases of the MICCAI CrossMoDA 2022 challenge, with best mean Dice similarity and average symmetric surface distance measures. Local contrast alteration of tumor appearances and iterative self-training with pseudo labels are likely to lead to performance improvements in a variety of segmentation contexts.

Rough Neutrosophic Multisets Geometric Aggregation Operator with Entropy Weight Combined Roughness Dice Similarity Measure and Its Application

Rough Neutrosophic Multisets Geometric Aggregation Operator with Entropy Weight Combined Roughness Dice Similarity Measure and Its Application

Exploring T-spherical fuzzy sets for enhanced evaluation of vocal music classroom teaching

Vocal music is a relatively complex skill and skill course, which not only plays an important role in music education in universities, but also cultivates students’ musical level and artistic cultivation. In the process of teaching vocal music in universities, how to improve the quality of teaching has become an important task and goal for teachers. The vocal music classroom teaching quality evaluation is a classical multiple-attribute group decision-making (MAGDM) problem. In this paper, some novel Dice similarity measures (DSM) and some generalized Dice similarity measures (GDSM) are proposed under T-spherical fuzzy sets (T-SFSs). Then, the weighted generalized Dice similarity measures (WGDSM) is used to solve the MAGDM with T-SFSs. Finally, an illustrative example for vocal music classroom teaching quality evaluation is given to demonstrate the efficiency of the GDSMs.

The generalized dice similarity measures for comprehensive evaluation of graphic design effects based on color psychology with t-spherical fuzzy sets

Traditional Chinese art is vast and profound, with various colors having rich meanings. The combination of colors can vividly and intuitively represent various characteristics of things. Fully reflecting the characteristics of traditional Chinese folk art in graphic design can achieve extremely strong expressive effects. In current graphic design, the artistic colors of traditional Chinese folk art have not yet been fully displayed, and there is a lack of understanding of the profound connotation of traditional Chinese art. The graphic design industry has a very broad development space. The comprehensive evaluation of graphic design effects based on color psychology is a classical multiple attribute group decision making (MAGDM) problems. In this work, we shall present some novel Dice similarity measures (DSM) of T-spherical fuzzy sets(T-SFSs) and the generalized Dice similarity measures (GDSM) of and indicates that the DSM and asymmetric measures (projection measures) are the special cases of the GDSM in some parameter values. Then, we propose the GDSM-based MAGDM models with T-SFSs. Then, we apply the GDSMs between T-SFSs to MAGDM. Finally, an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the GDSMs. The main contributions of this paper are summarized: (1) some novel Dice similarity measures (DSM) and the generalized Dice similarity measures (GDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed; (2) The weighted Dice similarity measures (WDSM) and the weighted generalized Dice similarity measures (WGDSMs) of T-spherical fuzzy sets(T-SFSs) are proposed to solve the MAGDM; (3) an illustrative example for comprehensive evaluation of graphic design effects based on color psychology is given to demonstrate the efficiency of the WGDSM; (4) Some comparative analysis are used to show the effectiveness of the proposed Dice similarity measures.

The cross-border e-commerce platform selection based on the probabilistic dual hesitant fuzzy generalized dice similarity measures

Abstract Cross-border e-commerce platform (CBECP) plays a very important role in the development of a cross-border e-commerce (CBEC). How to select the best CBECP scientifically and reasonably is a very critical multi-attribute group decision-making (MAGDM) issue. With the uncertainty of people’s cognition of the objective world, the decision-making process is full of a lot of fuzzy information. In view of the great advantages of probabilistic dual hesitation fuzzy set (FS) in expressing decision-making information, and in combination with the very extensive use of the Dice similarity measure (DSM), a new MAGDM method is proposed for the optimal CBECP selection (CBECPS) under the probabilistic dual hesitation fuzzy (PDHF) environment. First, on the basis of reviewing a large number of documents on the CBECPS for CBEC, the evaluation index system for the CBECPS is constructed; second, several new DSMs are proposed in the PDHF environment; third, based on the two newly proposed probabilistic dual hesitant weighted generalized Dice similarity measures, two novel MAGDM methods are provided for CBECPS, which are used for CBECPS; finally, the two established MAGDM techniques are compared with the existing decision-making methods, and the parameter analysis is carried out to illustrate the effectiveness and superiority of the two established MAGDM techniques. The two established techniques can not only be used for CBECPS of CBEC, but also be extended to similar related research.

External clustering validity index based on extended similarity measures

Clustering is one of the most important unsupervised approaches to organizing data in many practical applications. The application of different methods to clustering one dataset generally results in different clusterings of that dataset; for that reason, there is a need for evaluating the similarities of various clustering methods. This can be accomplished with the help of external validity indices. Although many external validity measures exist in the literature, most of them suffer from some drawbacks: they apply very strong null hypotheses, which lead to the independence of the clusterings, fixed number of clusters, and fixed cluster sizes. In addition, an acceptable measure should have some properties to be a metric. If a measure does not qualify for being a metric, the interpretation will not be easy. In this study, a novel Set Matching Index (SMI) is proposed based extended derivation of Jaccard, Dice, and Cosine measures. Thus, three new indices are introduced in this paper, i.e., Set Matching Index based on Extended Jaccard similarity measure (SMI-EJ), Set Matching Index based on Extended Dice similarity measure (SMI-ED) and Set Matching Index based on Extended Cosine similarity measure (SMI-EC) as a set matching metric. All of the metric properties are proved in the present research. For the assessment of the proposed indices performance quality, they are experimented on ten real-world and Synthetic datasets, and then the obtained results are compared with those of four popular external indices (NMI, RI, ARI and Purity) and a new index (PSI). Also to verify the performance of three proposed similarity indices, the experiments develop. We apply the proposed indices to the generated clustering solutions and compare the results. We show that the proposed indices achieve desirable results based on the external validity index properties.Moreover, SMI has simple formula with moderate and appropriate results that can be easily interpreted.

Analysis and Applications of Artificial Intelligence in Digital Education Based on Complex Fuzzy Clustering Algorithms

Digital education is very important and valuable because it is a subpart of artificial intelligence, which is used in many real-life problems. Digital education is the modern utilization of digital techniques and tools during online purchasing, teaching, research, and learning and is often referred to as technology-enhanced learning or e-learning programs. Further, similarity measures (SM) and complex fuzzy (CF) logic are two different ideas that play a very valuable and dominant role in the environment of fuzzy decision theory. In this manuscript, we concentrate on utilizing different types of dice SM (D-SM) and generalized dice SM (GD-SM) in the environment of a CF set (CFS), called CF dice SM (CFD-SM), CF weighted dice SM (CFWD-SM), CF generalized dice SM (CFGD-SM), and CF weighted generalized dice SM (CFWGD-SM), and also derived associated outcomes. Furthermore, to evaluate or state the supremacy and effectiveness of the derived measures, we aim to evaluate the application of artificial intelligence in digital education under the consideration of derived measures for CF information and try to verify them with the help of several examples. Finally, with the help of examples, we illustrate the comparison between the presented and existing measures to show the supremacy and feasibility of the derived measures.

A New Approach to Multiple Criteria Decision-Making Using the Dice Similarity Measure under Fermatean Fuzzy Environments

<p>Many contemporary multiple criteria decision-making (MCDM) problems are rather complicated and uncertain to manage. MCDM problems can be complex because they involve making decisions based on multiple conflicting criteria, and they can be uncertain because they often involve incomplete or subjective information. This can make it difficult to determine the optimal solution to the problem. Over the last decades, tens of thousands MCDM methods have been proposed based on fuzzy sets (FSs) and intuitionistic fuzzy sets (IFSs). In this paper, we propose a new MCDM method based on Fermatean fuzzy sets (FFSs) and improved Dice similarity measure (DSM) and generalized Dice similarity measures (GDSM) between two FFSs with completely unknown weights of criteria. When a decision matrix is given, we calculate the weights of criteria using a normalized entropy measure while the weights of criteria are not given by the decision-maker. Then, we use the proposed improved DSM and GDSM between two FFSs that take the hesitancy degree of elements of FFSs into account and develop a new MCDM method. Finally, we use the values of the proposed improved DSM and GDSM between two FFSs to get the preference order of the alternatives. The proposed method can overcome the drawbacks and limitations of some existing methods that they cannot get the preference order of the alternatives under Fermatean fuzzy (FF) environments.</p> <p> </p>

Several similarity measures of probabilistic dual hesitant fuzzy sets and their applications to new energy vehicle charging station location

Probabilistic dual hesitant fuzzy set (PDHFS) can better reflect the hesitant attitude and probability information of decision-makers than existing fuzzy sets. Therefore, it is a more powerful and important tool to express and deal with uncertain information. The score function of fuzzy number plays a very important role in comparing two fuzzy numbers. Entropy measure is a very effective tool for weighting decision attributes in multi-attribute group decision-making problems. Firstly, depending on the detailed analysis of the already existed score function of probabilistic dual hesitant fuzzy element (PDHFE), we built a novel score function for it, it is more effective and convenient when comparing two elements. Secondly, probabilistic dual hesitant fuzzy entropy was built to weight decision attributes. Thirdly, a combined weighting method was proposed which can reflect both subjective and objective information for decision attribute weighting based on the principle of minimum discriminant information. Fourthly, we extended the classical Dice and Jaccard similarity measures to the probabilistic dual hesitant fuzzy environment, and an all-new multi-attribute group decision-making technique to solve the problem that the decision attribute information is probabilistic dual hesitant fuzzy element. Finally, the applicability and effectiveness of this technique are proved by numerical examples and comparative analysis.

Vector Similarity Measures of Dual Hesitant Fuzzy Linguistic Term Sets and Their Applications

The dual hesitant fuzzy linguistic term set (DHFLTS) is defined by two functions that express the grade of membership and the grade of non-membership using a set of linguistic terms. In the present work, we first quote an example to point out that the existing complement operation of DHFLTS is on the wrong track. Meanwhile, we redefine this operation to fill the holes in the existing ones. Next, the notion of information energy under a dual hesitant fuzzy linguistic background is provided in order to build the criteria weight determination model. To further facilitate the theory of DHFLTS, we propose two vector similarity measures, i.e., Jaccard and Dice similarity measures, and their weighted forms for DHFLTS. In addition, we pioneer some generalized similarity measures of DHFLTSs and indicate that the Dice similarity measures are particular instances of the generalized similarity measures for some parameter values. Afterward, the similarity measures-based model with unknown weight information under the background of dual hesitant fuzzy linguistic environment is constructed. Lastly, an illustrated example is included to validate the method’s application, along with sensitivity analysis and comparative analysis, demonstrating the practicality and validity of its results.

Ranking Type-2 Intuitionistic Fuzzy Sets Based on Dice Similarity Measures

Ranking fuzzy numbers is an important part of decision-making in a fuzzy domain. Similarity measures are one of the widely used methods for sorting fuzzy numbers. In this paper, a Dice similarity measure is proposed for type-2 intuitionistic fuzzy sets (T2IFSs) and the properties of similarity measure are verified. The ranking in the proposed method is accomplished by comparing the given set to the best possible set under consideration. The proposed measure is tested on a wide range of T2IFSs and the results are analyzed with existing ranking methods. The comparison shows that the proposed method outperforms some of the shortcomings of existing ranking techniques and is thus effective in ranking.

Improving the Accuracy of Brain Tumor Identification in Magnetic Resonanceaging using Super-pixel and Fast Primal Dual Algorithm

Brain tumors are one of the most common causes of death that have been widely investigated by scholars in research areas, including care and prevention. Despite various empirical studies on the brain tumor segmentatin, there is still a need for further investigation. This fact is more needed in the automatic methods of brain tumors detection. In the present study, a new method for improving brain tumor segmentation accuracy based on super-pixel and fast primal dual (PD) algorithms has been proposed. The proposed method detects brain tumor tissue in Flair-MRI imaging in BRATS2012 dataset. This method detects the primary borders of tumors using a super-pixel algorithm, and improves brain tumor borders using fast PD in Markov random field optimization. Then, post-processing processes are used to delete white brain areas. Finally, an active contour algorithm was employed to display tumor area. Different experiments were carried on the proposed method and qualitative and quantitative criteria such as dice similarity measure, accuracy and F-measure were used for evaluation. The obtained results showed the efficiency of the proposed method, such that in the accuracy and sensitivity of 86.59 and 88.57% and F1-Measure 86.37 were obtained, respectively.

Standardized preprocessed derivatives for the Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS‐ND) Study

AbstractBackgroundThe Comprehensive Assessment of Neurodegeneration and Dementia (COMPASS‐ND) cohort of the pan‐Canadian Consortium on Neurodegeneration in Aging (CCNA) provides imaging on participants following a standardized protocol. This imaging contribution to deep phenotyping is done to study the full spectrum of age‐related dementias in more than 1,100 individuals (50‐90y) with subjective or mild cognitive impairment, Alzheimer’s disease, other dementias, or otherwise cognitively unimpaired. To accelerate discovery, we propose to release biomarkers extracted from these acquisitions. Here we present preliminary (N=385, AD=39) preprocessing methods, quality control, and available derivatives of the resting‐state functional (rsfMRI), diffusion weighted (DWI), and structural magnetic resonance images.MethodfMRI data was preprocessed using fMRIprep v20.2.1. Sørensen–Dice similarity measure was computed between individual masks and group templates using our internal BIDS app. Functional connectome of 101 regions from Dictionary of Functional Modes (DiFuMo) 64 dimension atlas were calculated with confounds removed. Mean network connectivity for the Yeo7 networks was summarized from connectomes. DWI data underwent robust quality control and preprocessing using Tractoflow. Bundle extraction and mean metric computation was done using freewater_flow, rbx_flow and tractometry_flow. Structural scans were preprocessed and segmented using iterativeN3 and MAGeT‐brain to obtain subcortical and cerebellar volumes. As a validation, a subset of variables associated with aging in the literature was tested for association with participant age using simple correlation.ResultThe functional Dice score was 0.915 (SD=0.017, N=390) and structural was 0.988 (SD=0.004, N=381). Age was significantly associated with mean DMN connectivity (r=‐0.226, p<0.001), bilateral inferior fronto‐occipital fasciculus freewater (r=0.533, p<0.001), and bilateral thalamus volume (r=‐0.243, p<0.001).ConclusionDerivatives obtained include: functional connectomes using DiFuMo64, network connectivity from Yeo7, DWI measures for major fiber tracts, and segmented subcortical and cerebellar volumes. The results of the quality and validity checks indicate the derivatives would be useful for the study of age‐related dementias. Full quality control and release of the complete COMPASS‐ND preprocessed imaging dataset and derivatives should be completed in 2022. For information on access see ccna‐ccnv.ca.

TOPSIS-method based on generalized dice similarity measures with hamy mean operators and its application to decision-making process

Hamy mean operator is the modified shape of the averaging and geometric aggregation operators, used for evaluation of the collection of finite numbers of attributes into a singleton set. Hamy mean operator is massively valuable and proficient principle to operate awkward and problematic information. In this analysis, we diagnosed the theory of Hamy mean operator based on complex Pythagorean fuzzy (CPF) sets, called CPF Hamy mean (CPFHM), CPF weighted Hamy mean (CPFWHM), CPF dual Hamy mean (CPFDHM), CPF weighted dual Hamy mean (CPFWDHM) operators and evaluate their dominant and valuable results as well as properties. Further, the TOPSIS “Technique for order of preference by similarity to ideal solution” method is also very valuable, computed in this manuscript based on generalized dice similarity measures to improve the worth of the diagnosed theory. Moreover, multi-attribute decision-making (MADM) and the TOPSIS method are two well-known techniques that are used for illustrating the beneficial optimal. Using these two tools, we discovered some numerical examples based on the evaluated operators, measures, and methods to show the feasibility and accuracy of the diagnosed information. Finally, we used some existing information and try to compare it with our evaluated information using our illustrated examples and discussed their geometrical interpretations.

Medical Diagnosis and Pattern Recognition Based on Generalized Dice Similarity Measures for Managing Intuitionistic Hesitant Fuzzy Information

Pattern recognition is the computerized identification of shapes, designs, and reliabilities in information. It has applications in information compression, machine learning, statistical information analysis, signal processing, image analysis, information retrieval, bioinformatics, and computer graphics. Similarly, a medical diagnosis is a procedure to illustrate or identify diseases or disorders, which would account for a person’s symptoms and signs. Moreover, to illustrate the relationship between any two pieces of intuitionistic hesitant fuzzy (IHF) information, the theory of generalized dice similarity (GDS) measures played an important and valuable role in the field of genuine life dilemmas. The main influence of GDS measures is that we can easily obtain a lot of measures by using different values of parameters, which is the main part of every measure, called DGS measures. The major influence of this theory is to utilize the well-known and valuable theory of dice similarity measures (DSMs) (four different types of DSMs) under the assumption of the IHF set (IHFS), because the IHFS covers the membership grade (MG) and non-membership grade (NMG) in the form of a finite subset of [0, 1], with the rule that the sum of the supremum of the duplet is limited to [0, 1]. Furthermore, we pioneered the main theory of generalized DSMs (GDSMs) computed based on IHFS, called the IHF dice similarity measure, IHF weighted dice similarity measure, IHF GDS measure, and IHF weighted GDS measure, and computed their special cases with the help of parameters. Additionally, to evaluate the proficiency and capability of pioneered measures, we analyzed two different types of applications based on constructed measures, called medical diagnosis and pattern recognition problems, to determine the supremacy and consistency of the presented approaches. Finally, based on practical application, we enhanced the worth of the evaluated measures with the help of a comparative analysis of proposed and existing measures.

The generalized dice similarity measures for bipolar complex fuzzy set and its applications to pattern recognition and medical diagnosis

The generalized dice similarity measures for bipolar complex fuzzy set and its applications to pattern recognition and medical diagnosis